Artificial Intelligence, Personalised Persuasion, and Climate Attitudes

Climate Information and Climate Concern

• The Problem: Persuading people to care about and act toward climate change is still one of the major collective challenges we face as a society

In Practice:

Why is this happening?

• The issue: Traditional information campaigns often fail because they don’t engage people personally
  • Psychological distance and intertemporal discounting reduce climate urgency (Trope & Liberman, 2010).
  • Emotional disconnection from abstract risks (Weber, 2006).

The (partial) solution:

• Tailored messaging aligned with individual beliefs and experiences is more effective

Our Solution

We argue that AI – and in particular its personalised content - could influence climate attitudes and overcome some of the limitations of pure information provision.

Key Advantages:

• From a practical perspective, AI can:
  • Scale: Reach thousands (but potentially millions) with personalised messages
  • Cost-effective: Reduce costs significantly
  • Engage: Create tailored conversations to the individual level
• From a theoretical perspective, AI can:
  • Evidence-based reasoning (Costello et al., 2025)
  • Non-judgmental interaction (Goel et al., 2024)
  • Responsive dialogue (Santoro et al., 2024)

Hypotheses

H1: When compared with the control condition (information provision), interacting with the LLM will have a positive effect on all outcomes of interest: a) climate concern, b) policy support, c) pro-environmental behavior, d) conversation spillover, e) political spillover, and f) subjective climate beliefs.

H2: The personalized LLM interaction on climate change will be more persuasive/effective than the non-personalized LLM interaction and the basic information provision on all outcomes of interest.

H3: The personalized LLM interaction on climate change condition will be more persuasive/effective compared to the personalized AI interaction on an unrelated political topic.

Research Design

• Online Survey Experiment
• Sample:
  • N = 1,500-2,000 UK adults (representative sample)
  • Recruited via established survey platform (Prolific) - the pipeline can be integrated into Qualtrics
  • Power analysis: 930 minimum for small-to-medium effect (d = 0.26-0.37)
  • Context: UK offers valuable setting where climate skepticism and polarisation is growing but not deeply entrenched

The treatments

Participants are randomized into these conditions:

1. Control: Standard information provision about climate change

2. Non-Personalized AI: Generic LLM conversation about climate change

3. Personalized AI (Unrelated Topic): Personalised AI-driven conversation about a political topic unrelated to climate change

4. Personalized AI (Climate): Personalised AI-driven conversation about climate change:

Survey Flow

An Example

Outcome Variables

• Climate concern: 5-point scale from “Not at all concerned” to “Extremely concerned”
• Pro-environmental behavior: Willingness to donate portion of $100 prize to climate NGO
• Conversation spillover: Likelihood of discussing AI conversation with others
• Political spillover: Choice to write message to Member of Parliament about climate change
• Subjective climate beliefs: Open-ended question about personal beliefs on climate change + LLM-generated Likert scale

Political Spillover

Field Experiment Integration

Information Seeking

Donations

Thank you!

Any Questions?

Appendix

The Numbers: